Inversion attack resilient zero-watermarking scheme for medical image authentication

Medical images are watermarked with patient data to enforce patient authentication and identification in radiology practices. In addition to common threats such as signal processing and geometric attacks, medical image watermarking systems are susceptible to a new class of threats called 'inversion attack', leading to ambiguities in establishing rightful ownership. This study presents an 'inversion attack' resilient zero-watermarking system, in the hybrid Contourlet transform singular value decomposition domain for medical image authentication. This scheme preserves the fidelity of the host image without introducing any artefacts and employs triangular number generating function and Hu's image invariants to confront 'inversion attacks'. The performance of the system is evaluated with medical images of different modalities and a quick response code watermark that contains patient data. The experimental results demonstrate the robustness of the system against 'ambiguity attacks' and signify its appropriateness for secured medical image exchange between remote radiologists.